Dynamic machine learning on premise model selection based on entity clustering and feedback

ABSTRACT

The disclosed technology relates to a process of providing dynamic machine learning on premise model selection. In particular, a set of machine learned models are generated and provided to an on premise computing device. The machine learned models are generated using a cluster of customer data (e.g. telemetric data) stored on a computing network having different ranges of computational complexity. One of the machine learned models from the set of machine learned models will be selected based on the current available computational resources detected at the on premise computing device. Different machine learned models from the set of machine learned models can then be selected based on changes in the available computational resources and/or customer feedback.

TECHNICAL FIELD

The subject matter of this disclosure relates in general to machinelearning, and more specifically to dynamic machine learning on premisemodel selection based on entity clustering and feedback.

BACKGROUND

Machine learning systems perform at their best capabilities when thesystems are able to use large amounts of data to train machine learnedmodels—using information that is not only coming from the customer butalso from a variety of other sources (e.g. other customers). This allowscloud-based machine learning systems to have an advantage over onpremise implementations where an associated computing device would havea more limited data set to work with.

In addition, the computing devices used for the on premiseimplementations may not always have the computational resources to trainmachine learned models locally compared to the vast amount of resourcesthat are available with the computing network (e.g., the cloud).Therefore, machine learned models may necessarily be generated andinitially trained within the cloud and subsequently provided to the onpremise computing device. However, this current solution of trainingmachine learned models for on premise implementations has some issues.First, there is the issue of confidentiality that prevents the use ofcustomer-specific information in the training of the cloud-based machinelearned models resulting in machine learned models that are less adaptedto the specific on premise computing device. Furthermore, once themachine learned models are provided to the on premise computing device,any further training that needs to be done to the machine learned model(e.g. updates) would need to go through the computing network (e.g., thecloud) again, which requires time and resources from both the cloud andthe on premise computing device.

BRIEF DESCRIPTION OF THE FIGURES

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described. Above willbe rendered by reference to specific embodiments that are illustrated inthe appended drawings. Understanding that these drawings depict onlyembodiments of the disclosure and are not therefore to be considered tobe limiting of its scope, the principles herein are described andexplained with additional specificity and detail through the use of theaccompanying drawings in which:

FIG. 1 is a conceptual block diagram illustrating an example networkenvironment in accordance with various embodiments of the subjecttechnology;

FIG. 2 shows an example method in accordance with various embodiments ofthe subject technology;

FIG. 3 illustrates an exemplary network device in accordance withvarious embodiments of the subject technology; and

FIG. 4 shows an example computing system in accordance with variousembodiments of the subject technology.

BRIEF DESCRIPTION OF EXAMPLE EMBODIMENTS

The detailed description set forth below is intended as a description ofvarious configurations of embodiments and is not intended to representthe only configurations in which the subject matter of this disclosurecan be practiced. The appended drawings are incorporated herein andconstitute a part of the detailed description. The detailed descriptionincludes specific details for the purpose of providing a more thoroughunderstanding of the subject matter of this disclosure. However, it willbe clear and apparent that the subject matter of this disclosure is notlimited to the specific details set forth herein and may be practicedwithout these details. In some instances, structures and components areshown in block diagram form in order to avoid obscuring the concepts ofthe subject matter of this disclosure.

OVERVIEW

Disclosed herein are computer-implemented methods, computer-readablemedia, and systems for providing machine learned models to an on premisecomputing device. The machine learned models are generated by firstclustering customer data based on similar telemetric parameters wherebythe customer data is stored on the computing network (e.g., the cloud).A number of different machine learned models are then generated usingthe clustered customer data. The machine learned models are thenprovided to the on premise computing device. The on premise computingdevice then selects one of the machine learned models from the generatedline learned models. In an embodiment, the telemetric parameterscorrespond to key performance indicators.

In addition, the generating of the different machine learned models canalso include generating machine learned models having different levelsof computational complexity. Furthermore, the on premise computingdevice detects current available computational resources at the onpremise computing device and selects a machine learned model from allthe generated machine learned models having a level of computationalcomplexity that corresponds to the current available computationalresources at the on premise computing device.

In another embodiment, changes in the current available computationalresources at the on premise computing device can be detected. Based onthe detected change in the available computational resources, adifferent machine learned model can be selected that has a level ofcomputational complexity corresponding to the detected change.

In another embodiment, feedback can be received from the customer at theon premise computing device that then directs the on premise computingdevice to select a different machine learned model based on the receivedfeedback. In some cases, the feedback corresponds to a performance ofthe on premise computing device using a currently selected machinelearned model.

In another embodiment, customer data can be retrieved from the onpremise data. The customer data from the on premise data can beclustered with the customer data stored on the computer network based onsimilar telemetric parameters. The cluster of the on premise customerdata and the customer data stored on the computer network can then beused to generate the plurality of machine learned models that will beprovided to the on premise computing device.

EXAMPLE EMBODIMENTS

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

As described herein, the present disclosure covers a proposed solutionfor providing dynamic machine learning on premise model selection. Inparticular, a variety of different machine learned models are generatedwithin a computing network (e.g., the cloud) using clusters of differenttelemetric data (e.g. key performance indicators) of different entities(e.g., other customers) that are similar to a computing device locatedon premise at the customer's location. The generated machine learnedmodels are also generated based c n different ranges of computationalcomplexity. The use of the telemetric data and computational complexityranges in generating the set of machine learned models provideflexibility at the on premise computing device by allowing the computingdevice to select which specific machine learned model based on thecurrent state of the on premise computing device and/or feedback fromthe customer to use rather than requesting new models to be trained fromthe computing network (e.g., the cloud). The on premise computing deviceis able to select the appropriate machine learned model from the set ofgenerated machine learned models as the current state of the on premisecomputing device (e.g., available computation resources) changes and/oradditional feedback is received from the customer. Further detailsregarding the subject technology will be provided below.

FIG. 1 is a conceptual block diagram illustrating an example networkenvironment 100 in accordance with various embodiments of the subjecttechnology. The example network environment 100 includes a computingnetwork (e.g., the cloud) 110. The computing network (e.g., the cloud)110 is communicatively connected to the on premise computing device 120located at a customer's location (e.g., place of business). Thecomputing network (e.g., the cloud) 110 is used to collect informationfrom other customer devices 170 in order to generate many differentmachine learned models that can be provided to the on premise computingdevice 120.

The computing network (e.g., the cloud) 110 includes a clustering unit130 that is used to generate the cluster of data that will be the basisfor the machine learned models. In an embodiment, the cluster of datacorresponds to similar telemetric data (such as key performanceindicators) that are shared between the other customer devices 170.Example telemetric data from the other customer devices 170 that areclustered may include (but are not limited to) the following: clientcount, detected interference, detected power, throughput, number ofclients performing onboarding, and average onboarding time althoughother types of telemetric data can also be used.

By using the cluster of data generated by the clustering unit 130, themodel generation unit 140 will generate the set of machine learnedmodels that will be provided to the on premise computing device 120.Each cluster of data will be the basis for a set of different machinelearned models.

The machine learned models that are generated by the computing network(e.g., the cloud) 110 and provided to the on premise computing devices120 are used by the on premise computing devices for a variety ofdifferent purposes. For example, the machine learned models can includepredictive analytics that can be used by the computing device 120 topredict when potential performance issues (e.g., network congestion) mayarise. The on premise computing devices can then utilize the machinelearned models to optimize user experiences by 1) addressing the issueswhen they occur and/or 2) modifying features of the computing device inorder to pre-emptively avoid the known issue.

A range of different machine learned models having different levels ofcomputational complexity will also be generated. For example, withreference to the same cluster of data, three different machine learnedmodel may be generated where a first machine learned model may have alow computational complexity, a second machine learned model would havea high computational complexity, and a third machine learned model wouldhave a medium computational complexity. It should be noted that more orless different varieties of machine learned models having differentthresholds of computational complexity may be implemented aside fromwhat was described above.

The machine learned models are generated having different levels ofcomputational complexity in order to provide the on premise computingdevice the flexibility of choosing a machine learned model that isappropriate based on the amount of available computational resources. Inan example scenario, an on premise computing device may experience highthroughput during typical business working hours (e.g. 8 am-5 pm).Therefore, there may not be much spare computational resources availableto utilize machine learned models that require higher computationalcomplexity without interfering with the current operations of the onpremise computing device. In contrast, the same on premise computingdevice may experience low throughput during non-work hours (e.g., 6 pmand later, or weekends) that would allow the same on premise computingdevice to utilize machine learned models with the high computationalcomplexity. By having different machine learned models be based ondifferent computational complexity, the appropriate machine learnedmodel can be chosen based on the available computational resources atthe on premise computing device.

By providing multiple machine learned models that the on premisecomputing device 120 can choose from, flexibility at the on premisecomputing device is possible. The on premise computing device is able toselect the appropriate machine learned model from the set of differentmachine learned models to use based on the current state of the onpremise computing device, that is specific to the on premise computingdevice and is done without requiring the interaction of the computingnetwork (e.g., the cloud).

This flexibility provided with the different machine learned model basedon the different cluster of data and computational complexity is animprovement over past prior implementations. Specifically, previously asingle machine learned model would be provided to on premise computingdevices. The single machine learned models lack the flexibility to adaptto the specifics of the on premise computing device and would requirethat a new machine learned model be provided when the previous machinelearned model no longer applied.

Furthermore, because the machine learned models are generated by thecomputing network (e.g. the cloud) 110 using data from other customers170, confidentiality and privacy of the on premise computing device 120can be preserved as no information needs to be shared between the onpremise computing device 120 and the computing network (e.g., the cloud)110 in order for the machine learned models to be generated/trained.Even though no information was shared, the on premise computing device120 may still be able to identify at least one of the machine learnedmodels from the set of machine learned models that is based on thecluster of data similar to that of the on premise computing device.

In some embodiments, there may be situations where the on premisecomputing device 120 would be willing to share its data with thecomputing network (e.g., the cloud) 110. In these scenarios, the cloud110 may be able to use the on premise computing device data to generatemachine learned models. Furthermore, the cloud 110 may also be able touse both the on premise computing device data with data from othercustomers to generate machine learned models.

After the computing network (e.g., the cloud) 110 generates the set ofmachine learned models based on the cluster of data and having differentranges of computational complexity, the set of machine learned models isprovided to the on premise computational device 120. From there, the onpremise computing device 120 will need to choose the appropriate machinelearned model from the provided set of machine learned models (via themodel selection application 160).

In order to facilitate the choice of the appropriate machine learnedmodel for the on premise computing device, the feedback unit 150monitors for and retrieves customer feedback about a current performanceof the on premise computing device. For example, a first machine learnedmodel ma have already been chosen by default. The customer feedbackregarding, for example, performance of the on premise computing deviceusing the chosen machine learned model may be positive or negative.Based on the customer feedback, different machine learned models fromthe set of machine learned models may be considered and/or chosen.

For example, in one scenario different machine learned models may beconsidered and chosen from the set of machine learned models as long asthe feedback unit 150 is receiving negative customer feedback. Once thefeedback unit 150 receives positive customer feedback, this may beindicative that the appropriate machine learned model has been chosenfor the on premise computing device 120.

In another embodiment, a ratio of positive and negative feedback can beaggregated for a pre-determined period of time to determine whether amachine learned model is appropriate for the on premise computing device120. A determination that the appropriate machine learned model ischosen is based on the receipt of positive feedback above apre-determined threshold (e.g., majority). In contrast, differentmachine learned models may be chosen if the receipt of negative feedbackis above the pre-determined threshold (e.g., majority). Other ways forusing customer feedback to determine when to select different machinelearned models is also possible.

In addition to using customer feedback via the feedback unit 150, themodel selection unit 160 will also detect the current state of the onpremise computing device, specifically the available computationalresources, to select an appropriate machine learned model. Depending onwhat processes are currently being run on the on premise computingdevice, different machine learned models may be chosen according to theavailable computing resources that can be assigned. For example, eachmachine learned model may be associated with a pre-determined thresholdamount of computational resources that would be needed in order toproperly utilize the associated machine learned model. In addition, theon premise computing device may also include an indication of whatamount of computational resources are available that can be devoted foruse with different machine learned models without affecting the existingprocesses currently being run on the on premise computing device.

FIG. 2 shows an example method 200 accordance with various embodimentsof the subject technology. The method 200 describes how a set of machinelearned models can be generated for an on premise computing device andhow the computing device is then able to select from the set of machinelearned models to use based on, for example, a current state of the onpremise computing device and/or feedback from the customer.

In step 210, data of other customer devices are clustered in thecomputing network (e.g., the cloud). This allows confidentiality andsecurity to be maintained at the on premise computing device as noinformation will be provided to the computing network. The data (e.g.,telemetric data) being clustered into groups share similarity and willbe used as a basis in generating the different machine learned modelsthat will be provided to the on premise computing device. The data thatwill be clustered, via step 210, include telemetric data (e.g., keyperformance indicators) used to characterize performance of each of theother customer computing devices. Example telemetric data include clientcount, detected interference, detected power, throughput, number ofclients performing onboarding, and average onboarding time. It should benoted that other types of telemetric data can also be used.

In step 220, a set of machine learned models are generated based on theclustered data. For example, multiple different machine learned modelsmay be created based on the use of throughputs the underlying data. Whenthe set of machine learned models is provided to the on premisecomputing device, it is not known which of the machine learned models ismost similar to the on premise computing device. By providing differentmachine learned models based on throughput, the on premise computingdevice is able to select the most appropriate machine learned model.

In addition, the set of models being generated using the clustered datawill also include variations of the machine learned models that takeinto account varying computational complexity. For example, multiplemodels using the same throughput clustered data can be generated takeinto account an amount of computational resources needed to use themachine learned model. Since it is unknown what computational resourcesmay be available at the on premise computing device, and theavailability of such resources can change over time at the on premisecomputing device, the variety of different machine learned models havingdifferent computational complexity provides the on premise computingdevice the ability to choose the model that corresponds to the availablecomputational resources.

The creation of the different machine learned models using the clustereddata and the different computational complexity provides the on premisecomputing device a lot of variety in being able to choose theappropriate machine learned model. Rather than requesting a new orupdated machine learned model from the computing network (e.g., thecloud) each time a current machine learned model being used becomesoutdated, the computing device is instead able to provide the on premisecomputing device with the set of machine learned models (in step 230).The on premise computing device is then able to go through the set ofmachine learned models and choose the appropriate machine learned modelwithout needing to again communicate with the computing network (e.g.,the cloud).

The choice of which machine learned model to choose will be based on,for example, detecting a current state of the on premise computingdevice (e.g., available resources) in step 240. Different models havingdifferent computational complexity can be chosen based on the availablecomputational resources at the on premise computing device. As theavailability of resources change over time, different machine learnedmodels can also be chosen.

Furthermore, in step 240, customer feedback can be received thatprovides an indication (e.g., negative or positive) whether a differentmachine learned model should be chosen. For example, a customer's userexperience may make note that the current performance of the on premisecomputing device is not satisfactory. This may be then used as anindication that a different machine learned model should be chosen. Byusing both the detected current state of the on premise computing deviceand/or customer feedback, an appropriate model is chosen in step 250.

FIG. 3 illustrates an exemplary network device 310 in accordance withvarious embodiments of the subject technology. Network device 310includes a master central processing unit (CPU) 362, interfaces 368, anda bus 315 (e.g., a PCI bus). When acting under the control ofappropriate software or firmware, the CPU 662 is responsible forperforming the steps illustrated in FIG. 2 . The CPU 362 preferablyaccomplishes all these functions under the control of software includingan operating system and any appropriate applications software. CPU 362may include one or more processors 363 such as a processor from theMotorola family of microprocessors or the MIPS family ofmicroprocessors. In an alternative embodiment, processor 363 isspecially designed hardware for controlling the operations of thenetwork device 310. In a specific embodiment, a memory 661 (such asnon-volatile RAM and/or ROM) also forms part of CPU 362. However, thereare many different ways in which memory could be coupled to the system.

The interfaces 368 are typically provided as interface cards (sometimesreferred to as “line cards”). Generally, they control the sending andreceiving of data packets over the network and sometimes support otherperipherals used with the network device 310. Among the interfaces thatmay be provided are Ethernet interfaces, frame relay interfaces, cableinterfaces, DSL interfaces, token ring interfaces, and the like. Inaddition, various very high-speed interfaces may be provided such asfast token ring interfaces, wireless interfaces, Ethernet interfaces,Gigabit Ethernet interfaces, ATM interfaces, HSSI interfaces, POSinterfaces, FDDI interfaces and the like. Generally, these interface'smay include ports appropriate for communication with the appropriatemedia. In some cases, they may also include an independent processorand, in some instances, volatile RAM. The independent processors maycontrol such communications intensive tasks as packet switching, mediacontrol and management. By providing separate processors for thecommunications intensive tasks, these interfaces allow the mastermicroprocessor 662 to efficiently perform routing computations, networkdiagnostics, security functions, etc.

Although the system shown in FIG. 3 is one specific network device ofthe present embodiments, it is by no means the only network devicearchitecture on which the present embodiments can be implemented. Forexample, an architecture having a single processor that handlescommunications as well as routing computations, etc. is often used.Further, other types of interfaces and media could also be used with therouter.

Regardless of the network device's configuration, it may employ one ormore memories or memory modules (including memory 661) configured tostore program instructions for the general-purpose network operationsand mechanisms for roaming, route optimization and routing functionsdescribed herein. The program instructions may control the operation ofan operating system and/or one or more applications, for example. Thememory or memories may also be configured to store tables such asmobility binding, registration, and association tables, etc.

FIG. 4 shows an example computing system 400 in accordance with variousembodiments of the subject technology. The example computing device cancorrespond to the computing devices associated with the customer's onpremise computing illustrated in FIG. 1 . Furthermore, the examplecomputing device may also make up any component thereof in which thecomponents of the system are in communication with each other usingconnection 405. Connection 405 can be a physical connection via a bus,or a direct connection into processor 410, such as in a chipsetarchitecture. Connection 405 can also be a virtual connection, networkedconnection, or logical connection.

In some embodiments computing system 400 is a distributed system inwhich the functions described in this disclosure can be distributedwithin a datacenter, multiple datacenters, a peer network, etc. In someembodiments, one or more of the described system components representsmany such components each performing some or all of the function forwhich the component is described. In some embodiments, the componentscan be physical or virtual devices.

Example system 400 includes at least one processing unit (CPU orprocessor) 410 and connection 405 that couples various system componentsincluding system memory 415, such as read only memory (ROM) 420 andrandom access memory (RAM) 425 to processor 410. Computing system 400can include a cache of high-speed memory 412 connected directly with, inclose proximity to, or integrated as part of processor 410.

Processor 410 can include any general purpose processor and a hardwareservice or software service, such as services 432, 434, and 436 storedin storage device 430, configured to control processor 410 as well as aspecial-purpose processor where software instructions are incorporatedinto the actual processor design. Processor 410 may essentially be acompletely self-contained computing system, containing multiple cores orprocessors, a bus, memory controller, cache, etc. A multi-core processormay be symmetric or asymmetric.

To enable user interaction, computing system 400 includes an inputdevice 445, which can represent any number of input mechanisms, such asa microphone for speech, a touch-sensitive screen for gesture orgraphical input, keyboard, mouse, motion input, speech, etc. Computingsystem 400 can also include output device 435, which can be one or moreof a number of output mechanisms known to those of skill in the art. Insome instances, multimodal systems can enable a user to provide multipletypes of input/output to communicate with computing system 400.Computing system 400 can include communications interface 440, which cangenerally govern and manage the user input and system output. There isno restriction on operating on any particular hardware arrangement andtherefore the basic features here may easily be substituted for improvedhardware firmware arrangements as the are developed.

Storage device 430 can be a non-volatile memory device and can be a harddisk or other types of computer readable media which can store data thatare accessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memories (RAMS), read only memory (ROM), and/or somecombination of these devices.

The storage device 430 can include software services, servers, services,etc., that when the code that defines such software is executed by theprocessor 410, it causes the system to perform a function. In someembodiments, a hardware service that performs a particular function caninclude the software component stored in a computer-readable mediumconnection with the necessary hardware components, such as processor410, connection 405, output device 435, etc., to carry out the function.

For clarity of explanation, in some instances the present technology maybe presented as including individual functional blocks includingfunctional blocks comprising devices, device components steps orroutines in a method embodied in software, or combinations of hardwareand software.

Any of the steps, operations, functions, or processes described hereinmay be performed or implemented by a combination of hardware andsoftware services or services, alone or in combination with otherdevices. In some embodiments, a service can be software that resides inmemory of a client device and/or one or more servers of a contentmanagement system and perform one or more functions when a processorexecutes the software associated with the service. In some embodiments,a service is a program, or a collection of programs that carry out aspecific function. In some embodiments, a service can be considered aserver. The memory can be a non-transitory computer-readable medium.

In some embodiments the computer-readable storage devices, mediums, andmemories can include a cable or wireless signal containing a bit streamand the like. However, when mentioned, non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implementedusing computer-executable instructions that are stored or otherwiseavailable from computer readable media. Such instructions can comprise,for example, instructions and data which cause or otherwise configure ageneral purpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network.The computer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware, orsource code. Examples of computer-readable media that may be used tostore instructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, solid state memory devices, flash memory, USB devices providedwith non-volatile memory, networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprisehardware, firmware and/or software, and can take any of a variety ofform factors. Typical examples of such form factors include servers,laptops smart phones, small form factor personal computers personaldigital assistants, and so on. Functionality described herein also canbe embodied in peripherals or add-in cards. Such functionality can alsobe implemented on a circuit board among different chips or differentprocesses executing in a single device, by way of further example.

The instructions, media for conveying such instructions, computingresources for executing them, and other structures for supporting suchcomputing resources are means for providing the functions described inthese disclosures.

Although a variety of examples and other information was used to explainaspects within the scope of the appended claims, no limitation of theclaims should be implied based an particular features or arrangements insuch examples, as one of ordinary skill would be able to use theseexamples to derive a wide variety of implementations. Further andalthough some subject matter may have been described in languagespecific to examples of structural features and/or method steps, it isto be understood that the subject matter defined in the appended claimsis not necessarily limited to these described features or acts. Forexample, such functionality can be distributed differently or performedin components other than those identified herein. Rather the describedfeatures and steps are disclosed as examples of components of systemsand methods within the scope of the appended claims.

The invention claimed is:
 1. A method for providing machine learnedmodels to an on premise computing device, the method comprising:clustering other customer data including similar telemetric parameters,wherein the customer data is stored on a computing network, the similartelemetric parameters including client count, detected interference,detected power, throughput, number of clients performing onboarding, andaverage onboarding time; generating, based on the clustered othercustomer data, at least first and second machine learned models, thefirst machine learned model requiring a predefined minimum of a firstamount of computational resources needed to execute the first machinelearned model, the second machine learned model requiring a predefinedminimum second amount of computational resources needed to execute thesecond machine learned model, the first amount being greater than thesecond amount; and providing the first and second machine learned modelsto an on premise computing device, wherein the on premise computingdevice: selects the first machine learned model in response to at leastthe available computational resources of the on premise computing devicebeing greater than the first amount of computational resources; receivesnegative feedback, from a customer, wherein the feedback states thecustomer's current user experience of the on premise device with thefirst machine learned model is unsatisfactory; and selects the secondmachine learned model in response to at least the received negativefeedback and the available computational resources of the on premisecomputing device being greater than the second amount of computationalresources.
 2. The method of claim 1, wherein the similar telemetricparameters comprise key performance indicators.
 3. The method of claim1, further comprising: retrieving customer data from the on premisecomputing device; clustering the customer data with the other customerdata based on similar telemetric parameters; and generating the firstand second machine learned models using the cluster of customer data andthe other customer data.
 4. The method of claim 1, wherein the first andsecond amounts of computational resources correspond to amounts ofthroughput experienced by the on premise computing device over a periodof time.
 5. A non-transitory computer-readable medium comprisinginstructions for providing machine learned models to an on premisecomputing device, the instructions, when executed by a computing system,cause the computing system to perform operations comprising: clusteringother customer data including similar telemetric parameters, wherein thecustomer data is stored on a computing network, the similar telemetricparameters including client count, detected interference, detectedpower, throughput, number of clients performing onboarding, and averageonboarding time; generate, based on the clustered other customer data,at least first and second machine learned models, the first machinelearned model requiring a predefined minimum of a first amount ofcomputational resources needed to execute the first machine learnedmodel, the second machine learned model requiring a predefined minimumof a second amount of computational resources needed to execute thesecond machine learned model, the first amount being greater than thesecond amount; and provide the first and second machine learned modelsto an on premise computing device, wherein the on premise computingdevice: selects the first machine learned model in response to at leastthe available computational resources of the on premise computing devicebeing greater than the first amount of computational resources; receivesnegative feedback, from a customer, wherein the feedback states thecustomer's current user experience of the on premise device with thefirst machine learned model is unsatisfactory; and selects the secondmachine learned model in response to at least the received negativefeedback and the available computational resources of the on premisecomputing device being greater than the second amount of computationalresources.
 6. The non-transitory computer-readable medium of claim 5,wherein the similar telemetric parameters comprise key performanceindicators.
 7. The non-transitory computer-readable medium of claim 5,wherein further instructions in the non-transitory computer readablemedium, when executed by the computing system, cause the computingsystem to: retrieve customer data from the on premise computing device;cluster the customer data with the other customer data based on similartelemetric parameters; and generate the first and second machine learnedmodels using the cluster of customer data and the other customer data.8. The non-transitory computer-readable medium of claim 5, wherein thefirst and second amounts of computational resources correspond toamounts of throughput experienced by the on premise computing deviceover a period of time.
 9. A system for providing machine learned modelsto an on premise computing device, the system comprising: a processor;and a non-transitory computer-readable medium storing instructions that,when executed by the system, cause the system to: cluster other customerdata including similar telemetric parameters, wherein the customer datais stored on a computing network, the similar telemetric parametersincluding client count, detected interference, detected power,throughput, number of clients performing onboarding, and averageonboarding time; generate, based on the clustered other customer data,at least first and second machine learned models for the cluster of theother customer data, the first machine learned model requiring apredefined minimum of a first amount of computational resources neededto execute the first machine learned model, the second machine learnedmodel requiring a predefined minimum of a second amount of computationalresources needed to execute the second machine learned model, the firstamount being greater than the second amount; and provide the first andsecond machine learned models to an on premise computing device, whereinthe on premise computing device: selects the first machine learned modelin response to at least the available computational resources of the onpremise computing device being greater than the first amount ofcomputational resources; receives negative feedback, from a customer,wherein the feedback states the customer's current user experience ofthe on premise device with the first machine learned model isunsatisfactory; and selects the second machine learned model in responseto at least the received negative feedback and the availablecomputational resources of the on premise computing device being greaterthan the second amount of computational resources.
 10. The system ofclaim 9, wherein the similar telemetric parameters comprise keyperformance indicators.
 11. The system of claim 9, wherein furtherinstructions in the non-transitory computer-readable medium, whenexecuted by the system, cause the system to: retrieve customer data fromthe on premise computing device; cluster the customer data with theother customer data based on similar telemetric parameters; and generatethe first and second machine learned models using the cluster ofcustomer data and the other customer data.
 12. The system of claim 9,wherein the first and second amounts of computational resourcescorrespond to amounts of throughput experienced by the on premisecomputing device over a period of time.